Cotrimoxazole-induced SIADH — a unique challenge during treatment of pulmonary nocardiosis

A 62 year old male non-smoker diagnosed with pulmonary nocardiosis was initiated on Cotrimoxazole therapy at a dose of 20 mg/kg per day in three divided doses. He developed hyponatremia (serum sodium 105 mEq/L) on day 3 of therapy. The potential causes of hyponatremia were evaluated. After ruling out other causes, the cause was suspected to be Cotrimoxazole-induced syndrome of inappropriate anti-diuretic hormone secretion (SIADH). We subsequently re-initiated therapy with Cotrimoxazole and the hyponatremia (serum sodium 110 mEq/L) recurred. Upon discontinuation of therapy, serum sodium levels returned to normal. The patient was started on Amoxycillin-Clavulanic Acid as an alternative therapy for pulmonary nocardiosis which resulted in resolution of the hyponatremia. Cotrimoxazole-induced SIADH is a rare occurrence. This case is representative of a patient with Cotrimoxazole-induced SIADH and the causal relationship was confirmed once resumption of therapy with the offending medi-cation resulted in hyponatremia. Clinicians should be aware of this rare adverse effect of Cotrimoxazole and should monitor serum electrolytes during therapy, especially in the elderly and in those receiving high doses

Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study

Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics.Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea’s centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric.Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period.Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic

Clinical outcome, viral response and safety profile of chloroquine in COVID-19 patients — initial experience

Introduction: Chloroquine and its analogues are currently being investigated for the treatment and post exposure prophylaxis of COVID-19 due to its antiviral activity and immunomodulatory activity.Material and methods: Confirmed symptomatic cases of COVID-19 were included in the study. Patients were supposed to receive chloroquine (CQ) 500 mg twice daily for 7 days. Due to a change in institutional protocol, initial patients received chloroquine and subsequent patients who did not receive chloroquine served as negative controls. Clinical effectiveness was determined in terms of timing of symptom resolution and conversion rate of reverse transcriptase polymerase chain reaction (RT-PCR) on day 14 and day 15 of admission.Results: Twelve COVID-19 patients formed the treatment arm and 17 patients were included in the control arm. The duration of symptoms among the CQ treated group (6.3 ± 2.7 days) was significantly (p-value = 0.009) lower than that of the control group (8.9 ± 2.2 days). There was no significant difference in the rate of RT-PCR negativity in both groups. 2 patients out of 12 developed diarrhea in the CQ therapy arm.  Conclusion: The duration of symptoms among the treated group (with chloroquine) was significantly lower than that of the control group. RT-PCR conversion was not significantly different between the 2 groups

Peripheral neuropathy in Chronic Obstructive Airway Disease

Peripheral neuropathy in COPD has received scanty attention despite the fact that very often clinicians come across COPD patients having clinical features suggestive of peripheral neuropathy while this comorbidity is often overlooked & considered a separate entity. A number of studies have now confirmed the association of COPD and peripheral neuropathy with hypoxaemia being a dominant etiopathogenic factor among others. We report a case a demyelinating polyradiculopathy in a patient with COPD along with a brief review of literature

Cotrimoxazole-Induced SIADH—A Unique Challenge during Treatment of Pulmonary Nocardiosis

A 62 year old male non-smoker diagnosed with pulmonary nocardiosis was initiated on Cotrimoxazole therapy at a dose of 20 mg/kg per day in three divided doses. He developed hyponatremia (serum sodium 105 mEq/L) on day 3 of therapy. The potential causes of hyponatremia were evaluated. After ruling out other causes, the cause was suspected to be Cotrimoxazole-induced syndrome of inappropriate anti-diuretic hormone secretion (SIADH). We subsequently re-initiated therapy with Cotrimoxazole and the hyponatremia (serum sodium 110 mEq/L) recurred. Upon discontinuation of therapy, serum sodium levels returned to normal. The patient was started on Amoxycillin-Clavulanic Acid as an alternative therapy for pulmonary nocardiosis which resulted in resolution of the hyponatremia. Cotrimoxazole-induced SIADH is a rare occurrence. This case is representative of a patient with Cotrimoxazole-induced SIADH and the causal relationship was confirmed once resumption of therapy with the offending medi-cation resulted in hyponatremia. Clinicians should be aware of this rare adverse effect of Cotrimoxazole and should monitor serum electrolytes during therapy, especially in the elderly and in those receiving high doses

Using a Simple Open-Source Automated Machine Learning Algorithm to Forecast COVID-19 Spread: A Modelling Study

Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics. Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea’s centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric. Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period. Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic

Emotional Distress Among Health Professionals Involved in Care of Inpatients with COVID-19: A Survey Based Cross-Sectional Study

Introduction: Health care workers (HCWs) are directly involved in processes linked with diagnosis, management, and assistance of coronavirus disease-19 (COVID-19) patients which could have direct implications on their physical and emotional health. Emotional aspects of working in an infectious pandemic situation is often neglected in favour of the more obvious physical ramifica-tions. This single point assessment study aimed to explore the factors related to stress, anxiety and depression among HCWs consequent to working in a pandemic. Material and Methods: This was a cross-sectional study involving healthcare workers who were working in COVID-19 inpatient ward, COVID-19 screening area, suspect ward, suspect intensive care unit (ICU) and COVID-19 ICU across four hospitals in India. A web-based survey questionnaire was designed to elicit responses to daily challenges faced by HCWs. The questionnaire was regressed using machine-learning algo-rithm (Cat Boost) against the standardized Depression, Anxiety and Stress Scale-21 (DASS 21) which was used to quantify emotional distress experienced by them. Results: A total of 156 participants were included in this study. As per DASS-21 scoring, severe stress was seen in ~17% of respondents. We could achieve an R2 of 0.28 using our machine-learning model. The major factors responsible for stress were decreased time available for personal needs, increasing age, being posted out of core area of expertise, setting of COVID-19 care, increasing duty hours, increasing duty days, marital status and being a resident physician. Conclusions: Factors elicited in this study that are associated with stress in HCWs need to be addressed to provide wholesome emotional support to HCWs bat-tling the pandemic. Targeted interventions may result in increased emotional resilience of the health-care system

Emotional distress among health professionals involved in care of inpatients with COVID-19: a survey based cross-sectional study

Introduction: Health care workers (HCWs) are directly involved in processes linked with diagnosis, management, and assistance of coronavirus disease-19 (COVID-19) patients which could have direct implications on their physical and emotional health. Emotional aspects of working in an infectious pandemic situation is often neglected in favour of the more obvious physical ramifications. This single point assessment study aimed to explore the factors related to stress, anxiety and depression among HCWs consequent to working in a pandemic. Material and methods: This was a cross-sectional study involving healthcare workers who were working in COVID-19 inpatient ward, COVID-19 screening area, suspect ward, suspect intensive care unit (ICU) and COVID-19 ICU across four hospitals in India. A web-based survey questionnaire was designed to elicit responses to daily challenges faced by HCWs. The questionnaire was regressed using machine-learning algorithm (Cat Boost) against the standardized Depression, Anxiety and Stress Scale — 21 (DASS 21) which was used to quantify emotional distress experienced by them. Results: A total of 156 participants were included in this study. As per DASS-21 scoring, severe stress was seen in ∼17% of respondents. We could achieve an R2 of 0.28 using our machine-learning model. The major factors responsible for stress were decreased time available for personal needs, increasing age, being posted out of core area of expertise, setting of COVID-19 care, increasing duty hours, increasing duty days, marital status and being a resident physician. Conclusions: Factors elicited in this study that are associated with stress in HCWs need to be addressed to provide wholesome emotional support to HCWs battling the pandemic. Targeted interventions may result in increased emotional resilience of the health-care system